There have been a number of algorithms proposed, PROSAC [1]}, P-NAPSAC [2]}, to find samples that consist of data points stemming from the same model instance early.
When fitting multiple instances to real-world data, it usually is a reasonable assumption that the points form spatially coherent structures [3]}, [4]}, [5]}, [2]}.
We propose a deterministic sampling that returns the connected components in a progressively densified neighborhood-graph as samples. The algorithm is shown in Alg. .
